69 citations · 175 across the 40 of their papers we have counts for
47 papers
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
Fangxin Wang, Peyman Baghershahi, Langzhou He +3
Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for of…
Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
Md Aminul Islam, Ahmed Sayeed Faruk, Sourav Medya +1
Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item signals over interaction graphs…
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
Peyman Baghershahi, Fangxin Wang, Debmalya Mandal +1
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) re…
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models
Angelo Zangari, Peyman Baghershahi, Sourav Medya
Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require reasoning over explicit structu…
Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support
Hasti Sharifi, Homaira Huda Shomee, Melissa Lamar +2
Older adults often struggle to articulate technology support needs due to unfamiliar technical terminology and age-related cognitive changes. We explore how large language models (…
SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems
Xin Wang, Pietro Lodi Rizzini, Sourav Medya +1
The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared…
Preference-driven Knowledge Distillation for Few-shot Node Classification
Xing Wei, Chunchun Chen, Rui Fan +3
Graph neural networks (GNNs) can efficiently process text-attributed graphs (TAGs) due to their message-passing mechanisms, but their training heavily relies on the human-annotated…
DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding
Zhu Wang, Homaira Huda Shomee, Sathya N. Ravi +1
In the field of design patent analysis, traditional tasks such as patent classification and patent image retrieval heavily depend on the image data. However, patent images -- typic…
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Peyman Baghershahi, Gregoire Fournier, Pranav Nyati +1
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation…
PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs
Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya
Large language models (LLMs) have emerged as transformative approaches in several important fields. This paper aims for a paradigm shift for patent writing by leveraging LLMs to ov…
COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
Gregoire Fournier, Sourav Medya
Graph neural networks (GNNs) have been widely used in various domains such as social networks, molecular biology, or recommendation systems. Concurrently, different explanations me…
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
Peyman Baghershahi, Sourav Medya
Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks. However, existing approaches are predominantly supervised, r…
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
Qize Jiang, Angelo Zangari, Linsey Pang +7
Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorp…
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
Rochana Chaturvedi, Peyman Baghershahi, Sourav Medya +1
Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the…
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
Weizhi Zhang, Liangwei Yang, Wooseong Yang +5
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with…
Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
Shishir Adhikari, Sourav Medya, Elena Zheleva
In causal inference, interference refers to the phenomenon in which the actions of peers in a network can influence an individual's outcome. Peer effect refers to the difference in…
BANGS: Game-Theoretic Node Selection for Graph Self-Training
Fangxin Wang, Kay Liu, Sourav Medya +1
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its…
Design Requirements for Human-Centered Graph Neural Network Explanations
Pantea Habibi, Peyman Baghershahi, Sourav Medya +1
Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However,…
A Survey on Patent Analysis: From NLP to Multimodal AI
Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi +1
Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent anal…
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang, Yuqing Liu, Kay Liu +3
Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inhere…
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Chirag Chhablani, Sarthak Jain, Akshay Channesh +2
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, maki…
VeriBug: An Attention-based Framework for Bug-Localization in Hardware Designs
Giuseppe Stracquadanio, Sourav Medya, Stefano Quer +1
In recent years, there has been an exponential growth in the size and complexity of System-on-Chip designs targeting different specialized applications. The cost of an undetected b…
COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
Hao Tian, Sourav Medya, Wei Ye
Combinatorial Optimization (CO) problems over graphs appear routinely in many applications such as in optimizing traffic, viral marketing in social networks, and matching for job a…
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Aritra Bhowmick, Mert Kosan, Zexi Huang +2
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology o…